The fields of statistical learning and machine learning are used to study problems of inference, which is to say gaining knowledge through the construction of models in order to make decisions or predictions based on observed data. In some problems, the observations can be represented as points in a Euclidean space with the L2-norm as a natural dissimilarity metric. Solutions to problems of dimensionality reduction, clustering, classification, and other learning tasks have been formulated using the Euclidean representation. Unfortunately, when no obvious natural Euclidean representation for the data is available, such inference tasks require independent solutions.
A straightforward strategy is to express the data in terms of a low dimensional feature vector for which the curse of dimensionality is alleviated. This initial processing of data as real-valued feature vectors in Euclidean space, which is often carried out in ad hoc manner, has been called the “dirty laundry” of machine learning. This procedure is highly dependent on having a good model for the data and in the absence of such model may be highly suboptimal, resulting in much information loss. When a statistical model is available, the process of obtaining a feature vector can be done optimally by extracting the model parameters for a given data set and thus characterizing the data through its lower dimensional parameter vector.
In clinical flow cytometry, cellular suspensions are prepared from patient samples (blood, bone marrow, solid tissue), and evaluated simultaneously for the presence of several expressed surface antigens and for characteristic patterns of light scatter as the cells pass through an interrogating laser. Antibodies to each target antigen are conjugated to fluorescent markers, and each individual cell is evaluated via detection of the fluorescent signal from each marker. The result is a characteristic multi-dimensional distribution that, depending on the panel of markers selected, may be distinct for a specific disease entity.
The data from clinical flow cytometry can be considered multi-dimensional both from the standpoint of multiple characteristics measured for each cell, and from the standpoint of thousands of cells analyzed per sample. Nonetheless, clinical pathologists generally interpret clinical flow cytometry results in the form of two-dimensional scatter plots in which the axes each represent one of multiple cell characteristics analyzed (up to 8 parameters per cell in routine clinical flow cytometry, and many more parameters per cell in research applications). Additional parameters are often utilized to “gate” (i.e. select or exclude) specific cell sets based on antigen expression or light scatter characteristics; however, clinical flow cytometry analysis remains a step-by-step process of 2-dimensional histogram analysis, and the multi-dimensional nature of flow cytometry is routinely underutilized in clinical practice.
Document classification is another problem that may be better understood when a document's multi-dimensional nature is taken into account. Recent work has shown interest in using dimension reduction for the purposes of document classification and visualization.
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.